# Mixture of ELM based experts with trainable gating network

arXiv: https://arxiv.org/abs/2105.11706 · HTML: https://mlsanity.com/paper/2105.11706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in MoE theory and gating functions, one of 12 directions in Mixture-of-experts.
- Builds on 0 of its 48 references that are in the map.
- First posted on arXiv 25 May 2021.

## Placed in

- Mixture-of-experts / [MoE theory and gating functions](https://mlsanity.com/moe/moe-theory-and-gating-functions/)

## Abstract (arXiv)

Mixture of experts method is a neural network based ensemble learning that has great ability to improve the overall classification accuracy. This method is based on the divide and conquer principle, in which the problem space is divided between several experts by supervisition of gating network. In this paper, we propose an ensemble learning method based on mixture of experts which is named mixture of ELM based experts with trainable gating network (MEETG) to improve the computing cost and to speed up the learning process of ME. The structure of ME consists of multi layer perceptrons (MLPs) as base experts and gating network, in which gradient-based learning algorithm is applied for training the MLPs which is an iterative and time consuming process. In order to overcome on these problems, we use the advantages of extreme learning machine (ELM) for designing the structure of ME. ELM as a learning algorithm for single hidden-layer feed forward neural networks provides much faster learning process and better generalization ability in comparision with some other traditional learning algorithms. Also, in the proposed method a trainable gating network is applied to aggregate the outputs of the experts dynamically according to the input sample. Our experimental results and statistical analysis on 11 benchmark datasets confirm that MEETG has an acceptable performance in classification problems. Furthermore, our experimental results show that the proposed approach outperforms the original ELM on prediction stability and classification accuracy.